{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Ovals of Cassini\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to Figure 4.8, The Cassini ovals are a family of quartic curves, sometimes referred to as Cassini ellipses, described by a point such that the product of its distances from two fixed points a distance apart is a constant [8]. For bistatic systems, the system performance may be analyzed by plotting the Cassini ovals for various signal-to-noise ratios.  The Cassini ovals are governed by (Equation 4.65)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation}\\label{eq:bistatic_range_product_polar}\n",
    "    (r_t\\, r_r)^2 = \\Big(\\rho^2 + (D/2)^2\\Big)^2 - \\rho^2\\, D^2\\, \\cos^2\\theta\n",
    "\\end{equation}\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting the library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the separation distance, ***D***, between the transmitting and receiving radars (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "separation_distance = 10e3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the system temperature (K), bandwidth (Hz), noise_figure (dB), transmitting and receiving losses (dB), peak transmit power (W), transmitting and receiving antenna gain (dB), operating frequency (Hz), and bistatic target RCS (dBsm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_temperature = 290\n",
    "\n",
    "bandwidth = 10e6\n",
    "\n",
    "noise_figure = 3\n",
    "\n",
    "transmit_losses = 4\n",
    "\n",
    "receive_losses = 6\n",
    "\n",
    "peak_power = 100e3\n",
    "\n",
    "transmit_antenna_gain = 30\n",
    "\n",
    "receive_antenna_gain = 28\n",
    "\n",
    "frequency = 1e9\n",
    "\n",
    "bistatic_target_rcs = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the number of points for plotting the Cassini ovals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_points = 100000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the parameters for the Cassini ovals equation\n",
    "$$ r ^ 4 + a ^ 4 - 2 a ^ 2 r ^ 2(1 + cos(2 \\theta)) = b ^ 4 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `linspace` and `log10` routines along with some constants from `scipy` for the angle sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace, log10\n",
    "from scipy.constants import pi, c, k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameter \"a\"\n",
    "\n",
    "a = 0.5 * separation_distance\n",
    "\n",
    "\n",
    "# Full angle sweep\n",
    "\n",
    "t = linspace(0, 2.0 * pi, number_of_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the bistatic range factor and use this along with the separation distance to calculate SNR<sub>0</sub> (where the factors ***a*** and ***b*** are equal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the wavelength (m)\n",
    "\n",
    "wavelength = c / frequency\n",
    "\n",
    "\n",
    "# Calculate the bistatic radar range factor\n",
    "\n",
    "bistatic_range_factor = (peak_power * transmit_antenna_gain * receive_antenna_gain * wavelength ** 2 * 10.0 ** (bistatic_target_rcs / 10.0)) / ((4.0 * pi) ** 3 * k * system_temperature * bandwidth * 10.0 ** (noise_figure / 10.0) * transmit_losses * receive_losses)\n",
    "\n",
    "\n",
    "# Calculate the signal to noise ratio at which a = b\n",
    "\n",
    "SNR_0 = 10.0 * log10(16.0 * bistatic_range_factor / separation_distance ** 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a list of the signal to noise ratios to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "SNR = [SNR_0 - 6, SNR_0 - 3, SNR_0, SNR_0 + 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the `matplotlib` routines for plotting the Cassini ovals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import `sqrt`, `sin`, `cos`, `real`, and `imag` from `scipy` for plotting the Cassini ovals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import sqrt, sin, cos, real, imag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the resulting Cassini ovals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-86298dab4ab6>:59: RuntimeWarning: invalid value encountered in sqrt\n",
      "  r = sqrt(a ** 2 * cos(2 * t) + sqrt(b ** 4 - a ** 4 * sin(2.0 * t) ** 2))\n",
      "<ipython-input-11-86298dab4ab6>:26: RuntimeWarning: invalid value encountered in sqrt\n",
      "  r1 = sqrt(a ** 2 * (cos(2.0 * t) + sqrt(cos(2 * t) ** 2 - 1.0 + (b / a) ** 4)))\n",
      "<ipython-input-11-86298dab4ab6>:28: RuntimeWarning: invalid value encountered in sqrt\n",
      "  r2 = sqrt(a ** 2 * (cos(2.0 * t) - sqrt(cos(2 * t) ** 2 - 1.0 + (b / a) ** 4)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "# Loop over all the desired signal to noise ratios\n",
    "\n",
    "for s in SNR:\n",
    "    \n",
    "\n",
    "    # Convert to linear units\n",
    "\n",
    "    snr = 10.0 ** (s / 10.0)\n",
    "\n",
    "    \n",
    "    # Parameter for Cassini ovals\n",
    "\n",
    "    b = (bistatic_range_factor / snr) ** 0.25\n",
    "\n",
    "\n",
    "    if a > b:\n",
    "\n",
    "\n",
    "         # Calculate the +/- curves\n",
    "\n",
    "         r1 = sqrt(a ** 2 * (cos(2.0 * t) + sqrt(cos(2 * t) ** 2 - 1.0 + (b / a) ** 4)))\n",
    "\n",
    "         r2 = sqrt(a ** 2 * (cos(2.0 * t) - sqrt(cos(2 * t) ** 2 - 1.0 + (b / a) ** 4)))\n",
    "\n",
    "\n",
    "\n",
    "         # Find the correct indices for imaginary parts = 0\n",
    "\n",
    "         i1 = imag(r1) == 0\n",
    "\n",
    "         i2 = imag(r2) == 0\n",
    "\n",
    "\n",
    "         r1 = real(r1)\n",
    "\n",
    "         r2 = real(r2)\n",
    "\n",
    "\n",
    "\n",
    "         # Plot both parts of the curve\n",
    "\n",
    "         label_text = \"SNR = {:.1f}\".format(s)\n",
    "\n",
    "         plt.plot(r1[i1] * cos(t[i1]), r1[i1] * sin(t[i1]), 'k.', label=label_text)\n",
    "\n",
    "         plt.plot(r2[i2] * cos(t[i2]), r2[i2] * sin(t[i2]), 'k.')\n",
    "\n",
    "\n",
    "    else:\n",
    "\n",
    "\n",
    "         # Calculate the range for the continuous curves\n",
    "\n",
    "         r = sqrt(a ** 2 * cos(2 * t) + sqrt(b ** 4 - a ** 4 * sin(2.0 * t) ** 2))\n",
    "\n",
    "\n",
    "         # Plot the continuous parts\n",
    "\n",
    "         label_text = \"SNR = {:.1f}\".format(s)\n",
    "\n",
    "         plt.plot(r * cos(t), r * sin(t), '.', label=label_text)\n",
    "\n",
    "\n",
    "    # Add the text for Tx/Rx locations\n",
    "\n",
    "    plt.text(-a, 0, 'Tx')\n",
    "\n",
    "    plt.text(a, 0, 'Rx')\n",
    "\n",
    "\n",
    "    # Set the plot title and labels\n",
    "\n",
    "    plt.title('Ovals of Cassini', size=14)\n",
    "\n",
    "    plt.xlabel('Range (km)', size=12)\n",
    "\n",
    "    plt.ylabel('Range (km)', size=12)\n",
    "\n",
    "\n",
    "    # Set the tick label size\n",
    "\n",
    "    plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "    # Turn on the grid\n",
    "\n",
    "    plt.grid(linestyle=':', linewidth=0.5)\n",
    "\n",
    "\n",
    "    # Add the legend\n",
    "\n",
    "    plt.legend(loc='upper left', prop={'size': 10})"
   ]
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